Action-Conditioned World Model for Goal Plane Guidance in Robotic Neck Ultrasound

arXiv:2607.21918 · cs.RO · Submitted 2026-07-24 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: We're looking at the paper: "Action-Conditioned World Model for Goal Plane Guidance in Robotic Neck Ultrasound".

Dev: Here's the gist of the paper. The gist: This framework presents an action-conditioned world model to guide goal plane probe navigation in robotic ultrasound by learning visual action dynamics from neck ultrasound trajectories and using this learned model to train a goal-conditioned action predictor.

Rosa: Problem Formulation Autonomous robotic ultrasound tasks often require large numbers of probe-motion trajectories for training, but collecting high-quality demonstrations is labor-intensive and explicit simulators are difficult to build because ultrasound appearance depends on contact, tissue deformation, and view-dependent acoustic artifacts<ref:2607.21918#pg2> The central challenge is that direct supervision and explicit simulation are both difficult to obtain at scale<ref:2607.21918#pg4> High quality expert trajectories and standard plane annotations are expensive, while physics based ultrasound simulators struggle to capture tissue deformation, acoustic shadowing, probe pressure and subject specific anatomy<ref:2607.21918#pg5> This motivates learning an internal actionconditioned model of ultrasound image dynamics and using it to guide action learning when demonstrations are sparse, noisy, or locally ambiguous<ref:2607.21918#pg6> Action-Conditioned Latent Diffusion World Model The proposed world model is introduced to make more effective use of large scale ultrasound scanning trajectories<ref:2607.21918#pg7> The model predicts the future ultrasound observation under a candidate probe motion and supplies a perceptual reward for a goal-conditioned action predictor<ref:2607.21918#pg8> The world model is implemented using a Conditional Diffusion Transformer (CDiT)

15: The function fθ models the ultrasound dynamics by predicting noise during the diffusion process The denoising objective of the CDiT is to minimize the mean squared error between the predicted noise and the true noise<ref:2607.21918#pg2> At inference stage, the model starts from Gaussian latent noise and iteratively denoises it under the specified contextaction-time condition<ref:2607.21918#pg4> The generated latent is then decoded into an ultrasound frame using the VAE decoder dec<ref:2607.21918#pg5> By modeling future observations as a conditional distribution instead of a deterministic regression target, the latent diffusion world model can better handle the ambiguity and appearance uncertainty caused by tissue deformation, probe pressure variation, and acoustic artifacts<ref:2607.21918#pg6> Goal-Conditioned Action Predictor The action predictor is designed to move the probe towards an ordered sequence of target standard planes rather than to imitate a single target motion<ref:2607.21918#pg8> The goal bank G = G1,G2,…,GK denotes one target standard plane that sonographer has defined Because a standard ultrasound plane can vary across patients, probe pressure, and small pose changes, each goal is represented by multiple latent samples instead of a single prototype The actor takes the recent latent context St−c+1:t, the corresponding previous relative actions and the goal bank G as input A temporal transformer then summarizes the recent scanning history into a context feature For each goal plane, the model projects all latent samples in Gk and pools them with learned sample weights, so the goal condition remains tied to several valid target appearances rather than a single averaged image The policy predicts a sequence of goal-conditioned corrective actions For each training action, we first identify the dominant axis as the dimension with the largest normalized action magnitude<ref:2607.21918#pg5> The motion value on the selected axis is then converted into one of five ordered bins<ref:2607.21918#pg5> The stop target is defined according to the target-plane offsets rather than simply marking the final step of a trajector<ref:2607.21918#pg6> Evaluation and Results Experiments on self collected neck ultrasound trajectories show that the world model preserves action dependent anatomical structure more reliably in target directed scans than in random sweeps<ref:2607.21918#pg7> In real-world closed loop experiments, the framework achieves success rates of 70.0% for carotid guidance and 65.0% for thyroid guidance<ref:2607.21918#pg8> The learned predictor can transfer from offline trajectory learning to the online robot execution and supports the role of the world model as a training signal The success rate on carotid and thyroid are 70% and 65.0% separately Discussion and Future Work The results suggest that the action-conditioned world model provides a useful training signal for robotic ultrasound guidance, but its scope remains limited It should therefore be interpreted as modeling visual action dynamics within the observed data distribution rather than as a complete physical simulator Future work could incorporate respiratory phase, temporal motion cues, or organ-specific state representations to separate robot-induced and anatomy-induced changes Incorporating force-torque measurements as an additional modality may enable pressure-aware dynamics and support actions that jointly optimize goal progress, contact stability, and patient safety This paper presented an action conditioned world model framework for goal plane probe guidance in robotic ultrasound<ref:2607.21918#pg4> The core idea is to learn visual action dynamics from neck ultrasound trajectories and use the frozen world model as an internal simulator for training a goal conditioned action predictor<ref:2607.21918#pg6> These results demonstrate the potential of learned ultrasound dynamics as a training signal for robot probe navigation Future work will incorporate force information to improve robustness across anatomies and scanning conditions Page 1 --- Action-Conditioned World Model for Goal Plane Probe Guidance in Robotic Ultrasound Siqi Fan Mingcong Chen Ran Liu Zixuan Yang Xiaoyu Fu Xiaoqing Gao Yunhui Liu Hongbin Liu Autonomous robotic ultrasound has been studied through visual servoing, reinforcement learning, task planners, and learned sonography skills<ref:2607.21918#pg2> Prior work has trained policies to navigate probes toward standard planes<ref:2607.21918#pg4>, robotized artery image acquisition<ref:2607.21918#pg5>, learned reward functions from limited demonstrations<ref:2607.21918#pg6>, and built high level robotic ultrasound agents for guideline driven procedures

8: –

11: <ref:2607.21918#pg6> This is limiting for long horizon probe guidance, since small changes in six degrees of freedom motion, contact and acoustic window can cause large and view dependent changes in ultrasound appearance<ref:2607.21918#pg7> The central challenge is that direct supervision and explicit simulation are both difficult to obtain at scale<ref:2607.21918#pg8> In contrast, image action image transitions are naturally produced during freehand or robotic scanning This motivates learning an internal actionconditioned model of ultrasound image dynamics and using it to guide action learning when demonstrations are sparse, noisy, or locally ambiguous Page 2 --- This paper studies a complementary setting for goal plane guidance in robotic neck ultrasound A learned world model predicts the future ultrasound observation under a candidate probe motion and supplies a perceptual reward for a goal-conditioned action predictor As shown in Fig.

Paper summary: Rosa: We’re looking at this paper again, "Action-Conditioned World Model for Goal Plane Guidance in Robotic Neck Ultrasound." The core thesis is that we can formulate robotic neck ultrasound guidance as learning action-conditioned acoustic image dynamics.

Dev: They propose a two-stage model based learning pipeline where the first part is a latent conditional diffusion world model that predicts future ultrasound observations from recent context frames, six-DoF relative probe motion, and temporal offset <ref:2607.21918#pg1,predicts future ultrasound observations from recent context frames>.

Taro: And the second part is a goal-conditioned temporal transformer that predicts the ordered probe motions, which are then fine-tuned using rewards computed from the frozen world model.

Rosa: It matters because they address the difficulty of obtaining large numbers of probe-motion trajectories for training and explain why explicit simulators are hard to build due to view-dependent acoustic artifacts and tissue deformation.

Dev: They claim that by modeling future observations as a conditional distribution instead of a deterministic regression target, this latent diffusion world model can handle the ambiguity caused by things like probe pressure variation and acoustic artifacts better.

Taro: The main contribution is formulating robotic neck ultrasound guidance as action-conditioned acoustic image dynamics learning, which lets them train the system when demonstrations are sparse or noisy.

Rosa: So, to put it plainly, they’re using learned visual action dynamics to guide a goal-conditioned action predictor in this specific ultrasound setting.

Conclusion: Dev: So, thinking about the title, "Action-Conditioned World Model for Goal Plane Guidance in Robotic Ultrasound," it really boils down to using the learned dynamics of the ultrasound images themselves as a guide for steering the robot's probe.

Rosa: And they did this by training a latent diffusion world model to predict what happens next under different motions, and then using that predictor to generate the correct sequence of moves toward a target plane.

Taro: What this means for the broader field is that we might be able to train goal-directed robotic probe navigation within protocol-constrained organ specific tasks without needing a perfect physical simulator upfront.

Dev: They suggest this framework provides a useful training signal for robotic ultrasound guidance, but they also caution that it should be seen as modeling visual action dynamics within the observed data distribution rather than a complete physical simulator.

Rosa: And they do acknowledge that future work could incorporate things like respiratory phase or temporal motion cues to better separate robot-induced changes from anatomy-induced changes.

Taro: Incorporating force-torque measurements could be another way to make the dynamics more pressure aware, which might help improve robustness across different anatomies and scanning conditions.

Siqi Fan, Mingcong Chen, Ran Liu, Zixuan Yang, Xiaoyu Fu, Xiaoqing Gao, Yunhui Liu

Chinese University of Hong Kong Department of Mechanical and Automation Engineering Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation King’s College London Department of Surgical and Interventional Engineering Xuanwu Hospital Capital Medical University

cs.RO

Submitted: 2026-07-24

Updated: 2026-10-21

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 82/100

The gist: The gist: This framework presents an action-conditioned world model to guide goal plane probe navigation in robotic ultrasound by learning visual action dynamics from neck ultrasound trajectories and

Key concepts

Action-Conditioned Latent Diffusion World Model: This is a model that predicts what the ultrasound image will look like in the future given a proposed probe motion. It uses a Conditional Diffusion Transformer (CDiT) to model this process, allowing it to handle uncertainties like tissue deformation and acoustic artifacts by predicting noise during the denoising process.
Goal-Conditioned Action Predictor: This component learns the sequence of corrective actions needed to move the probe toward a set of target planes. Instead of imitating one motion, it uses a 'goal bank'—multiple latent samples representing different valid target appearances—to condition its prediction, ensuring the probe aims for the correct plane.
Frozen World Model as Training Signal: The learned world model acts as an internal simulator that is kept fixed during training. This frozen model provides a reliable perceptual reward signal to train the goal-conditioned action predictor. This allows the predictor to learn robust navigation strategies based on realistic, learned visual dynamics.
Goal Bank (G): The goal bank consists of multiple latent samples representing different possible appearances of the target standard planes. This approach makes the goal condition flexible; instead of relying on one averaged image, the model conditions its action prediction on several valid target appearances, improving robustness.

Terminology

World Model — 5× in this paper · explained in 7 episodes
Action-Conditioned World Model — 2× in this paper · explained in 1 episode
Transformer — 1× in this paper · explained in 2 episodes

Summary

The gist: This framework presents an action-conditioned world model to guide goal plane probe navigation in robotic ultrasound by learning visual action dynamics from neck ultrasound trajectories and using this learned model to train a goal-conditioned action predictor.

Problem Formulation

Autonomous robotic ultrasound tasks often require large numbers of probe-motion trajectories for training, but collecting high-quality demonstrations is labor-intensive and explicit simulators are difficult to build because ultrasound appearance depends on contact, tissue deformation, and view-dependent acoustic artifacts<ref:2607.21918#pg2> The central challenge is that direct supervision and explicit simulation are both difficult to obtain at scale<ref:2607.21918#pg4> High quality expert trajectories and standard plane annotations are expensive, while physics based ultrasound simulators struggle to capture tissue deformation, acoustic shadowing, probe pressure and subject specific anatomy<ref:2607.21918#pg5> This motivates learning an internal actionconditioned model of ultrasound image dynamics and using it to guide action learning when demonstrations are sparse, noisy, or locally ambiguous<ref:2607.21918#pg6>

Action-Conditioned Latent Diffusion World Model

The proposed world model is introduced to make more effective use of large scale ultrasound scanning trajectories<ref:2607.21918#pg7> The model predicts the future ultrasound observation under a candidate probe motion and supplies a perceptual reward for a goal-conditioned action predictor<ref:2607.21918#pg8>

The world model is implemented using a Conditional Diffusion Transformer (CDiT) [15] The function fθ models the ultrasound dynamics by predicting noise during the diffusion process The denoising objective of the CDiT is to minimize the mean squared error between the predicted noise and the true noise<ref:2607.21918#pg2> At inference stage, the model starts from Gaussian latent noise and iteratively denoises it under the specified contextaction-time condition<ref:2607.21918#pg4> The generated latent is then decoded into an ultrasound frame using the VAE decoder dec<ref:2607.21918#pg5> By modeling future observations as a conditional distribution instead of a deterministic regression target, the latent diffusion world model can better handle the ambiguity and appearance uncertainty caused by tissue deformation, probe pressure variation, and acoustic artifacts<ref:2607.21918#pg6>

Goal-Conditioned Action Predictor

The action predictor is designed to move the probe towards an ordered sequence of target standard planes rather than to imitate a single target motion<ref:2607.21918#pg8> The goal bank G = G1,G2,…,GK denotes one target standard plane that sonographer has defined Because a standard ultrasound plane can vary across patients, probe pressure, and small pose changes, each goal is represented by multiple latent samples instead of a single prototype The actor takes the recent latent context St−c+1:t, the corresponding previous relative actions and the goal bank G as input A temporal transformer then summarizes the recent scanning history into a context feature For each goal plane, the model projects all latent samples in Gk and pools them with learned sample weights, so the goal condition remains tied to several valid target appearances rather than a single averaged image The policy predicts a sequence of goal-conditioned corrective actions For each training action, we first identify the dominant axis as the dimension with the largest normalized action magnitude<ref:2607.21918#pg5> The motion value on the selected axis is then converted into one of five ordered bins<ref:2607.21918#pg5> The stop target is defined according to the target-plane offsets rather than simply marking the final step of a trajector<ref:2607.21918#pg6>

Evaluation and Results

Experiments on self collected neck ultrasound trajectories show that the world model preserves action dependent anatomical structure more reliably in target directed scans than in random sweeps<ref:2607.21918#pg7> In real-world closed loop experiments, the framework achieves success rates of 70.0% for carotid guidance and 65.0% for thyroid guidance<ref:2607.21918#pg8> The learned predictor can transfer from offline trajectory learning to the online robot execution and supports the role of the world model as a training signal The success rate on carotid and thyroid are 70% and 65.0% separately

Discussion and Future Work

The results suggest that the action-conditioned world model provides a useful training signal for robotic ultrasound guidance, but its scope remains limited It should therefore be interpreted as modeling visual action dynamics within the observed data distribution rather than as a complete physical simulator Future work could incorporate respiratory phase, temporal motion cues, or organ-specific state representations to separate robot-induced and anatomy-induced changes Incorporating force-torque measurements as an additional modality may enable pressure-aware dynamics and support actions that jointly optimize goal progress, contact stability, and patient safety

Conclusion

This paper presented an action conditioned world model framework for goal plane probe guidance in robotic ultrasound<ref:2607.21918#pg4> The core idea is to learn visual action dynamics from neck ultrasound trajectories and use the frozen world model as an internal simulator for training a goal conditioned action predictor<ref:2607.21918#pg6> These results demonstrate the potential of learned ultrasound dynamics as a training signal for robot probe navigation Future work will incorporate force information to improve robustness across anatomies and scanning conditions

--- Page 1 ---

Action-Conditioned World Model for Goal Plane Probe Guidance in Robotic Ultrasound Siqi Fan Mingcong Chen Ran Liu Zixuan Yang Xiaoyu Fu Xiaoqing Gao Yunhui Liu Hongbin Liu Autonomous robotic ultrasound has been studied through visual servoing, reinforcement learning, task planners, and learned sonography skills<ref:2607.21918#pg2> Prior work has trained policies to navigate probes toward standard planes<ref:2607.21918#pg4>, robotized artery image acquisition<ref:2607.21918#pg5>, learned reward functions from limited demonstrations<ref:2607.21918#pg6>, and built high level robotic ultrasound agents for guideline driven procedures [8]–[11]<ref:2607.21918#pg6> This is limiting for long horizon probe guidance, since small changes in six degrees of freedom motion, contact and acoustic window can cause large and view dependent changes in ultrasound appearance<ref:2607.21918#pg7> The central challenge is that direct supervision and explicit simulation are both difficult to obtain at scale<ref:2607.21918#pg8> In contrast, image action image transitions are naturally produced during freehand or robotic scanning This motivates learning an internal actionconditioned model of ultrasound image dynamics and using it to guide action learning when demonstrations are sparse, noisy, or locally ambiguous

--- Page 2 ---

This paper studies a complementary setting for goal plane guidance in robotic neck ultrasound A learned world model predicts the future ultrasound observation under a candidate probe motion and supplies a perceptual reward for a goal-conditioned action predictor As shown in Fig.

Improvements for AI systems

Action-conditioned world model for goal plane probe guidance allows robotic ultrasound systems to learn visual action dynamics by predicting future ultrasound observations from recent context frames, probe motions and temporal offset. This enables the system to better handle the ambiguity and appearance uncertainty caused by tissue deformation, probe pressure variation, and acoustic artifacts during navigation.
Goal-conditioned action predictor provides a mechanism for inferring corrective motions using a goal bank where each goal is represented by multiple latent samples instead of a single prototype. This allows the policy to produce ordered probe motions guided by the protocol, improving accuracy on various motion dimensions like rotation and translation.
The two-stage training pipeline, involving supervised imitation followed by fine-tuning with world model rewards, ensures that the learned policy is both structurally sound and goal-directed. Stage 2 training refines specific residual corrections without degrading the action structure learned during supervised training, leading to improved bin accuracies for lateral translation and rotational correction.
Real-world deployment enables closed-loop guidance where the predictor executes discrete corrective motions based on live frames, achieving success rates of 70% for carotid guidance and 65.0% for thyroid guidance. This demonstrates the potential of learned ultrasound dynamics as a training signal for robot probe navigation.